200826 Expt.296: Sequence-Agnostic RNP Purification¶

NB: This demonstration analysis was performed on unpublished development data. The original experiment was designed to investigate the conditions under which RBP-RNA complexes could be purified on silane. The data shown here is only one part of 4 datasets in the experiment (including the protocol which worked)

Aims:
A. To investigate different buffer compositions, temperature and elution conditions that may reveal a capacity for the purification of RBP-RNA complexes via selective precipitation.

Method:
See associated writeup for Expt.296. There are 4 MQ datasets for this experiment totalling n=16 tested conditions, in this tutorial we will work with one dataset encompassing n=4 conditions.
In this particular experiment the sample names refer to:

20/30 = 20% or 30% ethanol in the capture and wash media
heat/obd = heated elution at 55deg vs on-bead digest. In this case the heated eluate was first taken, followed by the on-bead digestion ON THE SAME SAMPLE.
pH7 = These samples were processed under pH7.8
Silane = the bead chemistry used, in this case MyOne Silane Dynabeads

#### Import necessary modules
import os
import pandas as pd
from pathlib import Path
import copy
import jwrangle
import jvis
import jinspect
import jtest
import jweb
from imp import reload
import upsetplot as upset
from Bio import SeqIO
import altair as alt

import numpy as np
import preprocess
import seaborn as sns

#### define working directories
cwd  = Path(os.getcwd())
base_path = Path(os.path.join(*cwd.parts[:cwd.parts.index('experiments')]))

#### MaxQuant proteinGroups & evidence files
MQ_folder = jwrangle.importMixedFiles(cwd / 'MaxQuant')
MQ_folder.keys()

pGroups = MQ_folder['proteinGroups.txt']
evidence = MQ_folder['evidence.txt']

#### Inspect MQ setup
MQ_folder['parameters.txt'].head(9)

C:\Users\smith.j\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3242: DtypeWarning: Columns (53,54,61) have mixed types.Specify dtype option on import or set low_memory=False.
  if (await self.run_code(code, result,  async_=asy)):
C:\Users\smith.j\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3242: DtypeWarning: Columns (321,322) have mixed types.Specify dtype option on import or set low_memory=False.
  if (await self.run_code(code, result,  async_=asy)):

2. Metadata Creation¶

Custom Functions
jwrangle.MQ_writeMetadata( )

Metadata tabulates the test conditions for ALL experiments that shared the same MQ search and thuss all experiments that comprise the MQ outputs. Metadata can also be done in a spreadsheet program. Here, I have created my metadata programmatically instead (I simply find this easier).
The metadata table gives users the opportunity to rename samples and define the experimental parameters for the data. This task can be expecially complex for MaxQuant because a unified output is generated even if distinctly separate experiments are searched as a batch and with different parameters applied.
The function jwrangle.MQ_writeMetadata( ) will take a metadata table, rename all samples in the proteinGroups and evidence files, assign alternative filenames, and save new copies to be used in future analyses.

#### Inspect column names
colnames = list(pGroups.columns.values)

#### Derive experiment names as a list
#### I gave the original samples some irritating names that I'd like to correct
experiment_names = []
for i in colnames:
    if 'Intensity ' in i:
        experiment_names.append(i.replace('Intensity ', ''))

new_expt_names = []
for i in experiment_names:
    i = i.replace('pH8S', 'pH7-S')
    i = i.replace('ne', 'ne-')
    i = i.replace('NC', 'nCL')
    i = i.replace('PC', '254')
    i = i.replace('heat', '-heat')
    i = i.replace('obd', '-obd')
    new_expt_names.append(i)
    
#### Create a list of associated conditions
#### These samples were mislabeled 'NC' and 'PC' (the latter is often short for PAR-CL, but these samples are 254nm crosslinked)
condition =  []
for i in new_expt_names:
    trim = i[3:-2]
    condition.append(trim)

#### Create a list of associated replicate identifiers
replicate = []
for i in new_expt_names:
    replicate.append(i.split('-')[-1])

#### Create a more reader friendly list of sample names
samples =  []
for i in new_expt_names:  
    samples.append(i[3:])
    
#### The MQ_groups dictionary assigns each condition to its MQ group parameter
MQ_groups = {'20silaneheat':['pH7-Silane-20-heat_nCL','pH7-Silane-20-heat_254'], 
             '30silaneheat':['pH7-Silane-30-heat_nCL','pH7-Silane-30-heat_254'],
             '20silaneobd':['pH7-Silane-20-obd_nCL','pH7-Silane-20-obd_254'],
             '30silaneobd':['pH7-Silane-30-obd_nCL','pH7-Silane-30-obd_254']}

#### We then use the condition list to create an MQ_groups list
Grp_parameters = []
for label in condition:
    for key, value in MQ_groups.items():
        if label in value:
            Grp_parameters.append(key)

exp_df = pd.DataFrame(
    {'experiment': experiment_names,
     'condition': condition,
     'replicate': replicate,
     'sample':samples,
     'measure':['Intensity']*len(samples),                  # adding this column allows our metadata file to be compatible with Proteus
     'MQgroups':Grp_parameters
    })

exp_df

MQ_expt296 = jwrangle.MQ_writeMetadata(pGroups, evidence, exp_df, 'e296', cwd)

3. Re-Annotate the MaxQuant pGroups with stable Gene IDs¶

Functions
jweb.mapAnyID( )
jwrangle.importMixedFiles( )

MaxQuant does a good job of assigning a Gene name to each protein group. Presumably these gene names come from the FASTA. However:

Sometimes it fails to find a gene name
Sometimes it will assign an ID that is not a gene or include out-of-place characters
It doesn't always seem to be consistent
If the gene name originates from the FASTA then repeating the MQ search with an updated FASTA is the only way to update the gene IDs.
Use of a mapping service will standardise the ID conversion practices between my datasets and those of others, including RNA-Seq.

To avoid these problems we will remap the Majority protein IDs to ENTREZ gene IDs. jweb.mapAnyID( ) will retrieve all possible genes for each protein group, and will also select a primary ID to singularly represent the group by a consistent method. This is a very flexible function, see help( ) for further explanation. From this point, the MQ 'Gene names' column will no longer be necessary. This function can also handle ID mapping to and from almost any convention.

Ensuring our proteins have a consistent gene naming strategy is essential for inter-experiment comparison and the later use of set methods. It also creates a standard that can be applied for accurately mapping RNA-Seq results and thus aid in future mapping of protein-RNA partners.

#### If not already loaded, read in the metadata-adjusted files
metadata = pd.read_csv(cwd / 'metadata' / 'e296_metadata.csv', index_col = 0)
pGroups = pd.read_csv(cwd / 'MaxQuant' / 'e296_proteinGroups_metalabeled.txt', delimiter = '\t')
evidence = pd.read_csv(cwd / 'MaxQuant' / 'e296_evidence_metalabeled.txt', delimiter = '\t')

C:\Users\smith.j\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3051: DtypeWarning: Columns (321,322) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
C:\Users\smith.j\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3051: DtypeWarning: Columns (53,54,61) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

#### Dynamically remap gene names in our proteinGroups file and save a copy
pGroups_remap = jweb.mapAnyID_gPro(pGroups['Majority protein IDs'].tolist(), splitstr = [';', '-'], geneProductType = 'protein', 
                              gConvertOrganism = 'hsapiens', gConvertTarget = 'ENTREZGENE', writetopath = [cwd, 'pGroups_remap'], writeTargetsAsList = 'NO')

#### If not already loaded, read in the remapped proteinGroups file
pGroups_remap = jwrangle.importMixedFiles(cwd / 'downloads' / 'pGroups_remap', dropSuffix = 'yes')
pGroups_remap.keys()

dict_keys(['id_map', 'query_map'])

#### jwrangle.importMixedFiles() returns a dictionary where keys = files. We want the 'id_map' table created by jweb.mapAnyID_gPro().
#### We'll rename the Query column and drop duplicates so the table can be merged with our proteinGroups table.
id_map = pGroups_remap['id_map'].rename(columns={'Query': 'Majority protein IDs'}).drop_duplicates()

#### Now use merge to add these new columns to our proteinGroups table
pGroups_map = pd.merge(pGroups, id_map, on='Majority protein IDs', how='left')

#### Check the tables are merged by viewing column elements from each.
pGroups_map[id_map.columns.tolist() + ['Peptide IDs']].head(2)

4. Review Contaminants by Sample¶

Functions
jinspect.MQ_getContaminants( )
MQ_getContaminants_sbplot( )
jwrangle.importMixedFiles( )

We can extract the conaminants from our proteinGroups file using jinspect.MQ_getContaminants( ). These extracted table will return log2(iBAQ values).
Contaminants can then be reviewed with _MQ_getContaminantssbplot( ).

#### Extract contaminants
contaminants = jinspect.MQ_getContaminants(pGroups_map, metadata)

#### Sort the metadata into a more intuitive order
metadata_sort = metadata.sort_values(by = ['MQgroups','condition', 'replicate'], ascending=[True, False, True])

#### Visually inspect contaminants  
jvis.MQ_getContaminants_sbplot(contaminants, metadata_sort, width = 3, length = 2, layout = 'single')

<Figure size 432x288 with 0 Axes>

Results: GOOD¶

Very few contaminants in the samples that were heat eluted. Many contaminants in the samples that were subjected to on-bead digest. This is to be expected if selective precipition, based on the presence of a protein-RNA complex, is occurring.
This is because the on-bead digest samples will contain mostly pure precipitated protein.

5. Assess Digestion Efficiency¶

Functions
jinspect.MQ_getMissedCleavages( )
jvis.CommonPalettesAsHex
jvis.BarPlotByGroup_sbplot( )

Assessing missed cleavages is an essential metric for understanding the quality of the tryptic digestion. This data is recorded in the evidence file.
_jinspect.MQgetMissedCleavages( ) will return a long form data table that can easily be used for plotting.
The dictionary jvis.CommonPalettesAsHex contains a number of palettes that are common to both matplotlib and ggplot (from R). These are provided to ensure consistency is easy to achieve across both languages.
We'll plot the missed cleavages with the generic function jvis.BarPlotByGroup_sbplot( )

#### Extract the missed cleavage data into a long form table for plotting
MissedCleavages = jinspect.MQ_getMissedCleavages(evidence, metadata, drop_contaminants = True)
MissedCleavages.sort_values(by=['expt','sample'], inplace = True)
MissedCleavages

### Select a colour palette  
cpal = jvis.CommonPalettesAsHex

set2_paired = []
for i in cpal['Set2_qual']:
    set2_paired.append(i)
    set2_paired.append(i)

#### Plot the grouped data points  
sns.set_style('whitegrid')
jvis.BarPlotByGroup_sbplot(MissedCleavages, x_col = 'group', y_col = '% Missed Cleavages', title = '% Missed Cleavages', pal = set2_paired)

<Figure size 432x288 with 0 Axes>

Results: Average¶

Digestion efficiency isn't great. Let's reduce the digestion volume by 0.5x and maintain the same trypsin concentration in future experiments

6. Remove Contaminants¶

Functions
jwrangle.MQ_getThreePassFilter( )
SeqIO.parse( )

After QC we no longer want the contaminants in our data. jwrangle.MQ_getThreePassFilter( ) will remove reverse peptides, contaminants, and only identified by site from MQ tables.
The filter will also accept customised exclusion lists in case users have added odd protein species to the search FASTA tables. In this particular experiment we added to the human FASTA, RNAse proteins and the large T antigen.
The former as 1) a check that dynamic range is not being overwhelmed and 2) as an quantitative spike-in control to compare tryptic efficiency and the sample recovery across samples following C18 cleanup.

#### Map the location of the custom FASTA elements
os.listdir(base_path / 'my_resources' / 'FASTA')  

#### Create a list of the non-human proteins that were added to the custom FASTA genome search. 
new_cont = []
with open(base_path / 'my_resources' / 'FASTA' / "custom_proteome_elements.fasta", "r") as handle:
    for record in SeqIO.parse(handle, "fasta"):
        new_cont.append(record.id.split('|')[1])

#### Remove all unwanted contaminants and IDs from the proteinGroups table      
pGroup_clean = jwrangle.MQ_getThreePassFilter(pGroups_map, custom_exclusion = new_cont)

#### Inspect the cleaned dataframe
pGroup_clean[['ENTREZGENE_gPro primary'] + [i for i in pGroup_clean.columns if 'iBAQ' in i]].head(2)

7. Drop Gene Duplicates and Filter Intensities by LFQ¶

Functions
jinspect.MQ_dropDuplicateIDs( )

The next step focuses on improving confidence in the quality of our data. This is done by applying jinspect.MQ_dropDuplicateIDs( ) which has the below effects:

Because one gene can have many proteins, sometimes Maxquant will create multiple proteinGroups for a single gene. As most of our analysis focuses on genes we'll trim the lowest quality proteinGroups duplicates from the table.
Standard LFQ defaults require a minimum of 2 peptide species, at least one of which must be unique, for quantitation to be applied. Intensity and iBAQ values, however, do not have such a minimum limit. I consider a 2 peptide minimum to be a wise filter but still have use for the Intensity and iBAQ values. Thus where the LFQ filter is applied all measurements that do not meet the minimum limit will be discarded. In short, if there isn't a companion LFQ value, there won't be an Intensity or iBAQ value either after filtering.
It has been documented that Match Between Runs suffers a high frequency of false peptide transfers (Lim, Paulo, Gygi 2019; doi: 10.1021/acs.jproteome.9b00492). At the protein level, however, this false transfer rate is greatly mitigated by the minimum peptide rule applied by the LFQ algorithm. This is another good reason for our filtering step.

#### Drop duplicates and apply LFQ filter
filter_dict = jinspect.MQ_dropDuplicateIDs(pGroup_clean, metadata, prefix = 'Peptides', ID = 'ENTREZGENE_gPro primary', pool = 'measure', drop_ID = 'None', 
                                            keep_PoolCalcs = False, applyLFQ_filter = ['Intensity', 'iBAQ'])

#### The df_keep value contains our targets, df_droprows conatins the discarded duplicates. Assign the df_keep value to a new variable and inspect.
pGroup_filtered = filter_dict['df_keep']
pGroup_filtered.head(2)

WARNING: jinspect.MQ_getMeasuredMeansByGroup() has not been checked

8. Review Sample Clustering by Group¶

Functions
jtest.getDistanceMatrix( )
jvis.MQ_showDendrogramQC_mplplot( )

A distance matrix function jtest.getDistanceMatrix( ) is provided for users who wish to apply different algorithms or create different visualisations.
I like the 'ward' method for distance calculations and using a dengrogram to confirm that clustering matches expectations and so use a prerolled function jvis.MQ_showDendrogramQC_mplplot( )

#### Confirm that clustering matches expectations
jvis.MQ_showDendrogramQC_mplplot(pGroup_filtered, 'Intensity', metadata, 'QC clustering: ', grid = 'YES', fsize = (8, 8))

Results: INTERESTING¶

Clustering follows the expected pattern for all heat eluted samples. For the on-bead digestion, the separation is ok though not as convincing; there are some 254 samples straying into the nCL groups under both 20% and 30% ethanol capture conditions.
Remember that on-bead digestion was performed on the same sample following heat elution. If selective elution (not selective precipitation) is occurring then this process will produce some difference; because that difference is not so
defined as for the heated eluate, however, we can expect the on bead digestion approach to be less discriminatory.

9. Analyse Normalisation Effects by Sample¶

Functions
jwrangle.Log2_ByPrefix( )
jwrangle.MQ_poolMulti( )
jvis.ViolinCompare_sbplot( )

Here we review normalisation effects on each sample within the condition groups; these are most easily interpreted after log2 transformation. We will transform all measures of interest with _jwrangle.Log2ByPrefix( ) and then pool all the values of interest, by condition, with _jwrangle.MQpoolMulti( ). The function _jvis.ViolinComparesbplot( ) will let use compare Intensity distribution on a per sample basis.

Normalisation is applied to LFQ values by MaxQuant and is a feature of its handling of label-free data. I've not seen a detailed explanation of how it works though so it is a leap of faith that Cox and Mann have selected an appropriate method.
Normalisation must be applied separately to nCL and cCL groups. This is unusual though necessary to avoid outrageous results caused by having groups with extreme differences. See expt.313 for evidence.

#### Log2 transform available intensity values.
pGroup_log2 = jwrangle.Log2_ByPrefix(pGroup_filtered, 'LFQ intensity')
pGroup_log2 = jwrangle.Log2_ByPrefix(pGroup_log2, 'iBAQ')
pGroup_log2 = jwrangle.Log2_ByPrefix(pGroup_log2, 'Intensity')
pGroup_log2.replace(0,np.nan, inplace=True)

#### Create a long form dataset for each desired grouping
pool_SampleIntensity = jwrangle.MQ_poolMulti(pGroup_log2, metadata, melt_list = ['Intensity', 'LFQ intensity'], group = 'condition')
pool_SampleIntensity.keys()

dict_keys(['pH7-Silane-20-heat_nCL', 'pH7-Silane-20-heat_254', 'pH7-Silane-30-heat_nCL', 'pH7-Silane-30-heat_254', 'pH7-Silane-20-obd_nCL', 'pH7-Silane-20-obd_254', 'pH7-Silane-30-obd_nCL', 'pH7-Silane-30-obd_254'])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-20-heat_nCL'], title = 'pH7-Silane-20-heat_nCL: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = ['#ff6666', '#99ccff'])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-20-heat_254'], title = 'pH7-Silane-20-heat_254: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = ['#ff6666', '#99ccff'])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-30-heat_nCL'], title = 'pH7-Silane-30-heat_nCL: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set3_qual'])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-30-heat_254'], title = 'pH7-Silane-30-heat_254: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set3_qual'])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-20-obd_nCL'], title = 'pH7-Silane-20-obd_nCL: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set3_qual'][4:])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-20-obd_254'], title = 'pH7-Silane-20-obd_254: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set3_qual'][4:])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-30-obd_nCL'], title = 'pH7-Silane-30-obd_nCL: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set1_qual'][2:])

#### Inspect the Intensity shifts generated by the LFQ normalisation algorithm. In MaxQuant Raw Intensity and iBAQ values are 
#### not subjected to the LFQ normalisation calculations so this is a good way to spot any gross violations
sns.set_style('whitegrid')
jvis.ViolinCompare_sbplot(pool_SampleIntensity['pH7-Silane-30-obd_254'], title = 'pH7-Silane-30-obd_254: Normalisation Effects', ylabel = 'Log2(Intensity)', palette = cpal['Set1_qual'][2:])

Result: GOOD¶

Normalisation of heat + nCL samples is a bit wobbly (yes thats the proper word). This is to be expected, as we can later confirm that these samples have very few peptides present.
Normalisation of the 30% EtOH > Silane > On bead digest is quite dramatic; likely the result of technical variability in the precipitation efficiency onto the silane beads. This variability, however, is not present in the 20% version.

10. Compare Intensity Distribution and Sequence Coverage¶

Functions
jwrangle.MQ_poolDataByCondition( )
jvis.BoxPlotByColumn_sbplot( )

Next we will compare intensity and sequence coverage between groups. Log2 transformation has already been performed so we need only use jwrangle.MQ_poolDataByCondition( ) to create the appropriate long form dataset for plotting with jvis.BoxPlotByColumn_sbplot( ).

#### Pool data into a single long form dataset
pooled_dfDropGroupOne = jwrangle.MQ_poolDataByCondition(pGroup_log2, metadata_sort, prefix_list = ['Intensity', 'Sequence coverage'])

#### Compare Intensity distribution using a box and whisker plot
sns.set_style('whitegrid')
jvis.BoxPlotByColumn_sbplot(pooled_dfDropGroupOne, 'Intensity: ', 'Intensity')

#### Compare Sequence coverage using a box and whisker plot
sns.set_style('whitegrid')
jvis.BoxPlotByColumn_sbplot(pooled_dfDropGroupOne, 'Sequence coverage: ', 'Sequence coverage %')

Result: GOOD¶

The greatest distinction between nCL and cCL conditions is given by the heated elution sammples.

11. Compare Sum Peptide Counts¶

Functions
jinspect.MQ_getSumBySample( )
jvis.BarPlotByGroup_sbplot( )

To sum the total peptides observed across all proteins use _jinspect.MQgetSumBySample( ). These sums will be returned as a modified metadata table.
Plotting these by group is easily done with jvis.BarPlotByGroup_sbplot( ). The plotting order is determined by the metadata ordering.
In this case we are inspecting the number of peptides detected after having removed contaminants- thus if some spike-in proteins were removed, i.e. in this case RNAse treatments, they will not contribute to the peptide count. To look at the replicability of these spike-ins, we would reach back to the 'df_droprows' table generated by jinspect.MQ_dropDuplicateIDs( ) in section 7.

#### Extract the total peptides observed per sample
metaStats = jinspect.MQ_getSumBySample(pGroup_log2, metadata_sort, freqList = ['Peptides'], measure = False)

#### Plot the sum peptides
sns.set_style('whitegrid')
jvis.BarPlotByGroup_sbplot(metaStats, x_col = 'condition', y_col = 'Peptides', title = 'Sum Peptides vs pH enrichment', pal = set2_paired,
                          errorbars = 'SEM')

<Figure size 432x288 with 0 Axes>

Results: Interesting¶

Consistent with suspicions from the sequence coverage and normalisation data, there are very few peptides in the nCL heated samples. These samples are juxtaposed by their cCL counterparts which have high yields similar to those of the on-bead digest condition.

12. Compare Unique Gene Counts¶

Functions
jinspect.MQ_getFrequencyBySample( )

One gene can encode for many proteins that often share regions of similarity. As for illumina-based RNA-Seq, however, shotgun proteomics can rarely assign a peptide species to a singular protein. In MaxQuant these are called proteinGroups. Because we have do not require protein-specific results, and gene identity is more stable, our gene count describes the groups to which our detected proteins have been be assigned. Thus gene here is being detected by protein product, just as it would be detected by RNA product in RNA Seq; none of these 3 are synonymous. To be clear, this is a count and not a measure.

Gene frequency is defined by the summed observations per protein regardless of intensity value and this data is extracted to our modified metadata with jinspect.MQ_getFrequencyBySample( ) .
A typical MQ search will yield identical protein counts (though different values) for Intensity and iBAQ*. LFQ frequencies will vary depending on the search settings:

In this case the MQ search has set LFQ values to be calculated on a min 2 peptide ratio (this is the default)**

Notes
* Why protein counts should be identical I don't know. The original iBAQ paper stipulates rules for the inclusion of a protein in the iBAQ calculation but MaxQuant doesn't seem to apply them.
** Previously I tested LFQ min ratio at 1 peptide. At 1 minimum peptide there was unexpected QC clustering. Possible explanations for this are explained in section 7 and are cleaned up by jinspect.MQ_dropDuplicateIDs( ) function. We can expect this function to greatly reduce qualifying IDs (~20% fewer), especially in the QE samples, but I think the trade-off is worth it because we gain 1) a more robust ID check and 2) the same search can be used for LFQ based checks of dynamic changes, i.e. comparing more than one group of cCL captures for biological changes.

#### Count the number of unique 
metaStats = jinspect.MQ_getFrequencyBySample(pGroup_log2, metaStats, freqList = ['Intensity', 'iBAQ', 'LFQ intensity'], measure = False)
metaStats

#### Plot the counts
sns.set_style('whitegrid')
jvis.BarPlotByGroup_sbplot(metaStats, x_col = 'condition', y_col = 'Intensity', title = '# Genes Detected By Group', pal = set2_paired, ylabel = 'Unique Genes', errorbars = 'SEM')

<Figure size 432x288 with 0 Axes>

Results: GOOD¶

The pattern of counts is similar for genes as it is for peptides.
I wonder if the identities of all cCL counts are similar. If so, does this suggest that the RNA anchor is responsible for dragging a crosslinked complex back into the heated solution, while free protein remains behind on the bead?
Were this the case, it would also suggest that the silane wash steps are important for parting RNA from non-crosslinked protein- thus the free protein is left behind on the bead to be found by the on-bead digest.

13. Assess Replicate Correlation¶

Functions
jwrangle.MQ_getSliceByPrefix( )
jvis.showPearsonRegression_altair( )

The function _jwrangle.MQgetSliceByPrefix( ) provides a convenient means of extracting values of a specific group.
We can then use _jvis.showPearsonRegressionaltair( ) to perform pairwise comparisons between each member of those groups. This function is specifically applied to genes with shared intensities- genes exclusive to one sample or the other, represented by vertical or horizontal datapoints, are plotted but excluded from the pearson calculation.

#### Extract the intensity values as a dictionary where keys = groups
Intensity_Dict = jwrangle.MQ_getSliceByPrefix(pGroup_log2, metadata, 'Intensity', group = 'condition', add_col = None)
Intensity_Dict.keys()

dict_keys(['pH7-Silane-20-heat_nCL', 'pH7-Silane-20-heat_254', 'pH7-Silane-30-heat_nCL', 'pH7-Silane-30-heat_254', 'pH7-Silane-20-obd_nCL', 'pH7-Silane-20-obd_254', 'pH7-Silane-30-obd_nCL', 'pH7-Silane-30-obd_254'])

#### Check replicate consistency across all within group pairs
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-20-heat_nCL'], mark_color = set2_paired[2])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-20-heat_254'], mark_color = set2_paired[2])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-20-obd_nCL'], mark_color = set2_paired[4])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-20-obd_254'], mark_color = set2_paired[4])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-30-heat_nCL'], mark_color = set2_paired[6])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-30-heat_254'], mark_color = set2_paired[6])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-30-obd_nCL'], mark_color = set2_paired[8])
jvis.showPearsonRegression_altair(Intensity_Dict['pH7-Silane-30-obd_254'], mark_color = set2_paired[8])

Results: GOOD¶

Replicates look good among 254nm cCL samples. More varied among the sparse nCL samples which should be expected.

14. Classify RBP¶

Functions
jinspect.MQ_getFrequencyByGroup()
jtest.MQ_applyClassifyRBP()

Before classifying our RBP we need to first tally the frequency with which each protein appears in each condition using _jinspect.MQgetFrequencyByGroup( )
Once done, we use _jtest.MQapplyClassifyRBP( ) to generate a dictionary from which each class can be reviewed or plotted.

#### Use the metadata and proteinGroups tables to count how many times a gene is identified in its group (/6). 
#### Here I demonstrate how we can count for all instances of Intensity, iBAQ and LFQ Intensity
pGroup_Freq = jinspect.MQ_getFrequencyByGroup(pGroup_log2, metadata, 'iBAQ', group = 'condition')
pGroup_Freq = jinspect.MQ_getFrequencyByGroup(pGroup_Freq, metadata, 'LFQ intensity', group = 'condition')
pGroup_Freq = jinspect.MQ_getFrequencyByGroup(pGroup_Freq, metadata, 'Intensity', group = 'condition')
pGroup_Freq[['ENTREZGENE_gPro primary'] + [i for i in pGroup_Freq.columns if 'Freq' in i]].head(2)

#### Now we can classify RBP; the console will report if all proteins/genes have been properly classified or not.
RBP_Dict_20heat = jtest.MQ_applyClassifyRBP(pGroup_Freq, 'LFQ intensity', metadata, '20silaneheat', 'pH7-Silane-20-heat_nCL', 'pH7-Silane-20-heat_254', 3, 
                                         add_cols = ['ENTREZGENE_gPro all', 'ENTREZGENE_gPro primary', 'ENTREZGENE_gPro name'])

All proteins classified = True

#### Now we can classify RBP; the console will report if all proteins/genes have been properly classified or not.
RBP_Dict_20obd = jtest.MQ_applyClassifyRBP(pGroup_Freq, 'LFQ intensity', metadata, '20silaneobd', 'pH7-Silane-20-obd_nCL', 'pH7-Silane-20-obd_254', 3, 
                                         add_cols = ['ENTREZGENE_gPro all', 'ENTREZGENE_gPro primary', 'ENTREZGENE_gPro name'])

All proteins classified = True

#### Now we can classify RBP; the console will report if all proteins/genes have been properly classified or not.
RBP_Dict_30heat = jtest.MQ_applyClassifyRBP(pGroup_Freq, 'LFQ intensity', metadata, '30silaneheat', 'pH7-Silane-30-heat_nCL', 'pH7-Silane-30-heat_254', 3, 
                                         add_cols = ['ENTREZGENE_gPro all', 'ENTREZGENE_gPro primary', 'ENTREZGENE_gPro name'])

All proteins classified = True

#### Now we can classify RBP; the console will report if all proteins/genes have been properly classified or not.
RBP_Dict_30obd = jtest.MQ_applyClassifyRBP(pGroup_Freq, 'LFQ intensity', metadata, '30silaneobd', 'pH7-Silane-30-obd_nCL', 'pH7-Silane-30-obd_254', 3, 
                                         add_cols = ['ENTREZGENE_gPro all', 'ENTREZGENE_gPro primary', 'ENTREZGENE_gPro name'])

All proteins classified = True

#### The results of this classifcation can be found in a dictionary of dataframes for each output
#### See help() for an explanation of each dataset
RBP_Dict_20heat.keys()

dict_keys(['Input_df_annStatus', 'Summary_df_annStatus', 'I', 'IIa', 'IIb', 'IIc', 'NC', 'ND'])

#### We want the most general overview which of our classes per treatment and so concatenate the results from each Summary_df_annStatus
RBP_Class = pd.concat([RBP_Dict_20heat['Summary_df_annStatus'], RBP_Dict_20obd['Summary_df_annStatus'], RBP_Dict_30heat['Summary_df_annStatus'], RBP_Dict_30obd['Summary_df_annStatus']], axis=0, join='outer')

#### And represent them in a barplot
from matplotlib import pyplot as plt

plt.figure('rbp class')
sns.set_style('whitegrid')
sns.set(font_scale=1.1)
ax = sns.countplot(x='MQgroup', hue = 'RBP Class', data=RBP_Class.sort_values(by = ['MQgroup', 'RBP Class']), palette = cpal['RBP_Class'], edgecolor = 'black')
ax.set_ylabel('Unique Gene Count')
ax.set_title('Unique Genes detected per RBP Class')

RBP_Class.head(1)

#### Because each subclass of the class II RBP are identified based on different statistical assumptions we can more closely inspect if those assumptions trend differently among the different pH treatments.
plt.figure('rbp class')
sns.set_style('whitegrid')
sns.set(font_scale=1.1)
ax = sns.countplot(x='MQgroup', hue = 'RBP subClass', data=RBP_Class[RBP_Class['RBP subClass']!=''].sort_values(by=['MQgroup','RBP subClass']), palette = cpal['Set3_qual'], edgecolor = 'black')
ax.set_ylabel('Unique Gene Count')
ax.set_title('Unique Genes detected per RBP Class')

RBP_Class.head(1)

Results: GOOD¶

Consistent with the nCL vs cCL count disparity among the heated eluates it would appear that Class I identified RBPs constitute the majority of IDs.
This relationshiup is far less convincing for the on-bead digest samples with the Class II being the majority, many of which depended on fold change calculations for identification.

15. Compare RBP Identity Between Ethanol Treatments¶

Here we use the dictionaries output by jtest.MQ_applyClassifyRBP( ) for the purposes of creating a Venn Diagram.

The heated eluate samples were the clear winners, though it's worth investigating whether there is a difference in the genes identified between the 20% and 30% ethanol salt-bridging conditions.
Current incongruities, i.e. the use of salt-free washes prior to elution and also later experimental evidence (expt.311) revealed that salt bridging is unlikely to be the silane capture mechanism;
rather that selective precipitation is the true mechanism of selection. Indeed, the selection of 20% and 30% ethanol used in this protocol was based on early investigation into selective
precipitation (Expt.229). Either way, let's compare the overlap between Class I RBPs identified in 20% vs 30% EtOH.

from matplotlib import pyplot as plt
import numpy as np
from matplotlib_venn import venn3, venn3_circles

plt.figure(figsize=(6,6))

v = venn3([set(RBP_Dict_20obd['NC']['ENTREZGENE_gPro primary']), 
           set(RBP_Dict_20heat['I']['ENTREZGENE_gPro primary']),
           set(RBP_Dict_30heat['I']['ENTREZGENE_gPro primary'])], 
           set_labels = ('On Bead Digest\nNot Classified', '20% Ethanol Capture\nHeated Elution', '30% Ethanol Capture\nHeated Elution'),
           alpha = 0.5)

c = venn3_circles([set(RBP_Dict_20obd['NC']['ENTREZGENE_gPro primary']), 
                   set(RBP_Dict_20heat['I']['ENTREZGENE_gPro primary']), 
                   set(RBP_Dict_30heat['I']['ENTREZGENE_gPro primary'])], linestyle='dashed')

c[0].set_lw(1.5)
c[0].set_ls('dotted')
c[1].set_lw(1.5)
c[1].set_ls('dotted')
c[2].set_lw(1.5)
c[2].set_ls('dotted')

for text in v.set_labels:
    text.set_fontsize(18)
for text in v.subset_labels:
    text.set_fontsize(14)

Results: GOOD¶

The overlap here is nearly complete. It is likely that 20% and 30% ethanol are largely equivalent.
The comparative lack of overlap with the group of non-classified genes from the on-bead digestion condition emphasizes that this similarity is unlikely to be random.

16. Review Basic Gene Ontology¶

Functions
jwrangle.importMixedFiles( )
jweb.fetchQuickGO_stats( )

In this section we will explore Gene Ontology (GO) memberships for the observed proteins. There is little use in applying statistical tests such as Gene Ontology Enrichment Analysis (GOEA) for these experiments; the combination of selection by RNA interaction and the comparative lack of deep RBP validation for many candidates would make such a study rather spurious. We can, however, investigate the frequency with which our identified RBPs appear in previous studies. In addition, we can use this frequency to further assess whether the 20% and 30% capture conditions used here are equivalent or not.

A number of GO-specific and utility functions are provided to help with retrieving Gene Ontologies from the QuickGo database. In this section we'll look at the most basic.
The function jweb.fetchQuickGO_stats( ) will fetch the annotation statistics for all records belonging to the gene ID from a submitted list. Reviewing these statistics before beginning an analysis is ideal, because it contextualises the breadth of future analyses. This function returns these statistics in the from of a dictionary which can be converted to a dataframe for easier viewing by using the dedicated function jweb.getQuickGO_stats( ).

In addition to contextualising the search space of subsequent analyses, fetching the annotation numbers is important for checking that the number of records, per GO ID, falls below 10000. This is because QuickGo will not allow larger searches to be done programmatically. If your GO ID of interest has many more records users should retrieve their records manually. These details will be covered fourther in the next section.

#### I like to keep a table of interesting GO terms in a local csv file, let's find it
MyResources = jwrangle.importMixedFiles(base_path / 'my_resources')
MyResources.keys()

dict_keys(['.ipynb_checkpoints', 'control_elements', 'control_proteins', 'custom_dfs', 'FASTA', 'GOexplore.ipynb', 'GO_TermsOfInterest.csv', 'GO_TermsOfInterest_stats.csv', 'Hentze_core_RBP_list.txt', 'Hentze_core_RBP_list_uniprot.txt', 'Hentze_core_RBP_plusE1_8.txt', 'instrumentQC', 'limma_normalizeTIs_p3550_p3592_p3657.nb.html', 'limma_normalizeTIs_p3550_p3592_p3657.Rmd', 'TIs_p3550_p3592_p3657.csv', 'TIs_p3550_p3592_p3657_loess.csv'])

MyResources['GO_TermsOfInterest.csv'].head(9)

#### Let's get basic statistics on the broader RNA-binding category "GO:0003723"  
MyGO_Stats_Dict = jweb.fetchQuickGO_stats(['GO:0003723'], QG_geneProductType = 'protein', QG_taxonId = '9606', QG_geneProductSubset = ['Swiss-Prot', 'TrEMBL'])
MyGO_Stats_Dict.keys()

dict_keys(['GO0003723_Swiss-Prot', 'GO0003723_TrEMBL'])

#### To view basic information, like the number of annotations associated with the GO term
RNA_Binding_stats = jweb.getQuickGO_stats(MyGO_Stats_Dict)
RNA_Binding_stats

Result: GOOD¶

The SwissProt and TrEMBL counts are below 10000 records so, conveniently, we can continue the tutorial with the simplest scenario for record extraction.

17. Retrieve GO Records¶

Functions
jweb.fetchQuickGO( )
jwrangle.concatGO_DataFrameDict( )
jweb.mapQuickGO( )
jwrangle.AnnotateDataFrameCtrls( )
jinspect.MQ_getFrequencyBySample( )

We can see above that none of our downloaded IDs have exceeded our fetch limit of 10000 records. If any did, we would need to manually retrieve the tsv file.
For the GO terms we wish to investigate to investigate jweb.fetchQuickGO( ) will fetch the relevant proteins and also apply the same onsistent gene ID mapping algorithm we used earlier when remapping the MaxQuant IDs. This process is important because it allows us to provide consistent gene IDs to subsequent sets analysis. Where the writetopath options is used, the results from any searches will be saved to a local downloads folder- this is useful as a snapshot of the annotations used at the time or as a simple way of avoiding fetching the records via API in the future.

The function jwrangle.concatGO_DataFrameDict( ) let's us concatenate a dictionary created by jweb.fetchQuickGO( ). This is useful when the user wishes to pool both SwissProt and TrEMBL records for a given

The function jwrangle.AnnotateDataFrameCtrls( ) taks our QuickGo records and annotates each protein or gene in a given dataframe (i.e. a proteinGroups table) for membership to the searched GO ID. The function jinspect.MQ_getFrequencyBySample( ) acts similarly to the peptide and gene count functions used earlier- it will sum the frequency of memberships to each GO catgeory and report the counts as a modified metadata table.

A Note on Manual Downloads
The jweb.fetchQuickGO( ) will also output to console the html address that one should use if a manual download if required; this address will encompass all the relevant record characteristics that typically used by this suite. Once this table has been downloaded, the function jweb.mapQuickGO( ) can be used to provide the same ID mapping service as performed in jweb.fetchQuickGO( ). The returned elements will also be manipulated into the same format.

#### Download and create a local copy of all the records associated with our GO query.  
QuickGo_dict = jweb.fetchQuickGO(['GO:0003723'], QG_geneProductType = 'protein', QG_taxonId = '9606', QG_geneProductSubset = ['Swiss-Prot', 'TrEMBL'], gConvertOrganism='hsapiens', writetopath= True)
QuickGo_dict.keys()

GO0003723_Swiss-Prot has 5245 records: 
https://www.ebi.ac.uk/QuickGO/annotations?goId=GO:0003723&taxonId=9606&taxonUsage=exact&goUsage=descendants&geneProductType=protein&geneProductSubset=Swiss-Prot

GO0003723_TrEMBL has 4976 records: 
https://www.ebi.ac.uk/QuickGO/annotations?goId=GO:0003723&taxonId=9606&taxonUsage=exact&goUsage=descendants&geneProductType=protein&geneProductSubset=TrEMBL

dict_keys(['GO0003723_Swiss-Prot', 'GO0003723_TrEMBL'])

### We'll assess our RBP frequency by considering both SwissProt and TrEmbl records, thus let's concatenate the dictionaries for GO:0003723
QuickGo_dict_concat = jwrangle.concatGO_DataFrameDict(QuickGo_dict)
QuickGo_dict_concat.keys()

dict_keys(['GO0003723'])

#### With our GO records in hand we next investigate whether our identified proteins are members of GO:OOO3723
#### This can be done with any DataFrame, but here we'll use the last version of our modified proteinGroups table 
pGroup_GO = jwrangle.AnnotateDataFrameCtrls(pGroup_Freq, QuickGo_dict_concat, search_match = 'ENTREZGENE_gPro primary', dict_match = 'ENTREZGENE_gPro primary', none_col = 'GO_None')
pGroup_GO.keys()

D:\MEGA\Programming\Scripts_JS\RBP_SUITE\modules\jwrangle.py:43: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ctrl_nohits_df[none_col] = True                                                                           #add zero annotation column to annotation columns

dict_keys(['ann_df', 'ann_subdf'])

#### The ann_df contains the master dataframe. The sub_df contains a slice for each positively identified Gene in each GO category searched.
#### Let's view what those membership annotations look like
pGroup_GO['ann_df'][['ENTREZGENE_gPro primary']+[i for i in list(pGroup_GO['ann_df'].columns) if 'GO' in i]].head(2)

#### Now to find out the counts for each GO ID being searched we will modify our metadata table in a fashion in the same way as for prptide and gene counting.
#### We'll use iBAQ for these counts but, given all intensities have previously been filtered by LFQ membership both 'Intensity' or 'LFQ intensity' would give the same result.
metaStatsGo = jinspect.MQ_getFrequencyBySample(pGroup_GO['ann_df'], metaStats, freqList = list(QuickGo_dict_concat.keys()) + ['GO_None'], measure = 'iBAQ')
metaStatsGo

#### To plot these counts
jvis.BarPlotByGroup_sbplot(metaStatsGo, x_col = 'condition', y_col = 'GO0003723', title = 'GO:0003723, RNA-Binding', pal = set2_paired)

<Figure size 432x288 with 0 Axes>

Results: GOOD¶

That's a lot of RNA-binding proteins. But, for further validation, it will be far more useful to calculate the relative memberships, i.e. GO:0003723 vs None.

#### Calculate the % GO0003723 members per group
metaStatsGo['% RBP'] = metaStatsGo.apply(lambda row: round(100*row['GO0003723']/(row['GO0003723']+row['GO_None'])), axis = 1)

#### Plot
jvis.BarPlotByGroup_sbplot(metaStatsGo, x_col = 'condition', y_col = '% RBP', title = 'GO:0003723, RNA-Binding', pal = set2_paired, yrange = [0,100])

<Figure size 432x288 with 0 Axes>

Results: GOOD¶

Well the % RNA binding also looks quite similar between both 20% vs 30% EtOH, and nCL vs 254, heated eluates. This would also be consistent with the notion:

the 20% or 30% EtOH formulas are near equivalent.
only proteins that is RNA tethered will be eluted; the proteins that came off in the nCL group are likely stubborn interactors.

The difference between nCL and cCL obd samples could be explained by the nCL and cCL interphases being of different composition prior to bead capture. Thus, as previous experiments have shown, there is some capacity to discriminate at the interphase though the challenge is in further purifying the RNPs.

Conclusion¶

Simply put, the present protocol works well for global capture but precise elution by gentle heating is key to selecting for proteins that are RNA-bound.

The protocol most probably functions on the basis of selective precipitation rather than by the commonly believed salt-bridging method.

This does not disqualify salt bridging from having a role in convetional RNA extraction- elimination of salts in these protocls releases free RNA.
This might indicates that the selective precipitative method here simultaneously removes any free RNA during the salt free wash- this would mean the RNPs are wholly purified. They are free of both untethered RNA and untethered protein.
This claim, however, needs to be proven. To do so most simply:
- Retain the first salt free wash. Quant the RNA, and SYPRO stain for protein.
- From the heated eluate, Quant the RNA and SYPRO for protein.
- Calculate the ratio of protein:RNA and compare between wash and eluate.

	Parameter	Value
0	Version	1.6.7.0
1	User name	smith.j
2	Machine name	MSCYPHER-253
3	Date of writing	08/26/2020 20:00:00
4	Include contaminants	True
5	PSM FDR	0.01
6	PSM FDR Crosslink	0.01
7	Protein FDR	0.01
8	Site FDR	0.01

	Majority protein IDs	ENTREZGENE_gPro all	ENTREZGENE_gPro primary	ENTREZGENE_gPro name	UNIPROT_gPro status	Peptide IDs
0	A0A024R4E5;Q00341;Q00341-2;H0Y394	HDLBP;None	HDLBP	high density lipoprotein binding protein [Sour...	SWISSPROT	57;711;1649;1671;1940;2102;2775;2824;3106;3440...
1	A0A024R4M0;P46781;B5MCT8;C9JM19	RPS9	RPS9	ribosomal protein S9 [Source:HGNC Symbol;Acc:H...	SWISSPROT	6085;6086;10517;11131;11450;13657;14430;14702;...

	ENTREZGENE_gPro primary	iBAQ	iBAQ pH7-Silane-20-heat_nCL-A	iBAQ pH7-Silane-20-heat_nCL-B	iBAQ pH7-Silane-20-heat_nCL-C	iBAQ pH7-Silane-20-heat_nCL-D	iBAQ pH7-Silane-20-heat_254-A	iBAQ pH7-Silane-20-heat_254-B	iBAQ pH7-Silane-20-heat_254-C	iBAQ pH7-Silane-20-heat_254-D	...	iBAQ pH7-Silane-20-obd_254-C	iBAQ pH7-Silane-20-obd_254-D	iBAQ pH7-Silane-30-obd_nCL-A	iBAQ pH7-Silane-30-obd_nCL-B	iBAQ pH7-Silane-30-obd_nCL-C	iBAQ pH7-Silane-30-obd_nCL-D	iBAQ pH7-Silane-30-obd_254-B	iBAQ pH7-Silane-30-obd_254-F	iBAQ pH7-Silane-30-obd_254-C	iBAQ pH7-Silane-30-obd_254-D
0	HDLBP	1.044400e+09	0.0	0.0	0.0	0.0	9.678600e+07	7.080900e+07	9.100200e+07	9.025000e+07	...	60376000.0	50760000.0	0.0	0.0	0.0	0.0	3174900.0	57683000.0	82764000.0	75414000.0
1	RPS9	1.689600e+10	0.0	0.0	0.0	0.0	1.421100e+09	1.117600e+09	1.870200e+09	2.090800e+09	...	399610000.0	324730000.0	993560.0	1235400.0	0.0	0.0	36901000.0	420100000.0	579340000.0	495440000.0

	Protein IDs	Majority protein IDs	Peptide counts (all)	Peptide counts (razor+unique)	Peptide counts (unique)	Protein names	Gene names	Fasta headers	Number of proteins	Peptides	...	iBAQ pH7-Silane-30-heat_nCL-C	iBAQ pH7-Silane-30-heat_nCL-D	iBAQ pH7-Silane-30-obd_254-B	iBAQ pH7-Silane-30-obd_254-C	iBAQ pH7-Silane-30-obd_254-D	iBAQ pH7-Silane-30-obd_254-F	iBAQ pH7-Silane-30-obd_nCL-A	iBAQ pH7-Silane-30-obd_nCL-B	iBAQ pH7-Silane-30-obd_nCL-C	iBAQ pH7-Silane-30-obd_nCL-D
0	A0A024R4E5;Q00341;Q00341-2;H0Y394;H7C0A4;C9JIZ...	A0A024R4E5;Q00341;Q00341-2;H0Y394	56;55;50;39;18;15;13;11;10;10;10;10;9;6;6;5;5;...	56;55;50;39;18;15;13;11;10;10;10;10;9;6;6;5;5;...	56;55;50;39;18;15;13;11;10;10;10;10;9;6;6;5;5;...	Vigilin	HDLBP	;;;	23	56	...	0.0	0.0	3174900.0	82764000.0	75414000.0	57683000.0	0.0	0.0	0.0	0.0
1	A0A024R4M0;P46781;B5MCT8;C9JM19;F2Z3C0;A8MXK4	A0A024R4M0;P46781;B5MCT8;C9JM19	16;16;13;13;4;4	16;16;13;13;4;4	16;16;13;13;4;4	40S ribosomal protein S9	RPS9	;;;	6	16	...	0.0	0.0	36901000.0	579340000.0	495440000.0	420100000.0	0.0	1235400.0	0.0	0.0

	experiment	condition	replicate	sample	measure	MQgroups	Peptides	Intensity	iBAQ	LFQ intensity
0	17_pH8Silane20heat_NC-A	pH7-Silane-20-heat_nCL	A	pH7-Silane-20-heat_nCL-A	Intensity	20silaneheat	176.0	30	30	30
1	18_pH8Silane20heat_NC-B	pH7-Silane-20-heat_nCL	B	pH7-Silane-20-heat_nCL-B	Intensity	20silaneheat	152.0	31	31	31
2	19_pH8Silane20heat_NC-C	pH7-Silane-20-heat_nCL	C	pH7-Silane-20-heat_nCL-C	Intensity	20silaneheat	151.0	49	49	49
3	20_pH8Silane20heat_NC-D	pH7-Silane-20-heat_nCL	D	pH7-Silane-20-heat_nCL-D	Intensity	20silaneheat	123.0	50	50	50
4	21_pH8Silane20heat_PC-A	pH7-Silane-20-heat_254	A	pH7-Silane-20-heat_254-A	Intensity	20silaneheat	13781.0	1254	1254	1254
5	22_pH8Silane20heat_PC-B	pH7-Silane-20-heat_254	B	pH7-Silane-20-heat_254-B	Intensity	20silaneheat	13719.0	1252	1252	1252
6	23_pH8Silane20heat_PC-C	pH7-Silane-20-heat_254	C	pH7-Silane-20-heat_254-C	Intensity	20silaneheat	13836.0	1284	1284	1284
7	24_pH8Silane20heat_PC-D	pH7-Silane-20-heat_254	D	pH7-Silane-20-heat_254-D	Intensity	20silaneheat	13909.0	1527	1527	1527
8	33_pH8Silane20obd_NC-A	pH7-Silane-20-obd_nCL	A	pH7-Silane-20-obd_nCL-A	Intensity	20silaneobd	9868.0	697	697	697
9	34_pH8Silane20obd_NC-B	pH7-Silane-20-obd_nCL	B	pH7-Silane-20-obd_nCL-B	Intensity	20silaneobd	11367.0	750	750	750
10	35_pH8Silane20obd_NC-C	pH7-Silane-20-obd_nCL	C	pH7-Silane-20-obd_nCL-C	Intensity	20silaneobd	11211.0	757	757	757
11	36_pH8Silane20obd_NC-D	pH7-Silane-20-obd_nCL	D	pH7-Silane-20-obd_nCL-D	Intensity	20silaneobd	11843.0	960	960	960
12	37_pH8Silane20obd_PC-A	pH7-Silane-20-obd_254	A	pH7-Silane-20-obd_254-A	Intensity	20silaneobd	15215.0	1177	1177	1177
13	38_pH8Silane20obd_PC-B	pH7-Silane-20-obd_254	B	pH7-Silane-20-obd_254-B	Intensity	20silaneobd	15251.0	1192	1192	1192
14	39_pH8Silane20obd_PC-C	pH7-Silane-20-obd_254	C	pH7-Silane-20-obd_254-C	Intensity	20silaneobd	15120.0	1208	1208	1208
15	40_pH8Silane20obd_PC-D	pH7-Silane-20-obd_254	D	pH7-Silane-20-obd_254-D	Intensity	20silaneobd	15301.0	1470	1470	1470
16	25_pH8Silane30heat_NC-A	pH7-Silane-30-heat_nCL	A	pH7-Silane-30-heat_nCL-A	Intensity	30silaneheat	259.0	53	53	53
17	26_pH8Silane30heat_NC-B	pH7-Silane-30-heat_nCL	B	pH7-Silane-30-heat_nCL-B	Intensity	30silaneheat	263.0	58	58	58
18	27_pH8Silane30heat_NC-C	pH7-Silane-30-heat_nCL	C	pH7-Silane-30-heat_nCL-C	Intensity	30silaneheat	274.0	64	64	64
19	28_pH8Silane30heat_NC-D	pH7-Silane-30-heat_nCL	D	pH7-Silane-30-heat_nCL-D	Intensity	30silaneheat	262.0	88	88	88
20	29_pH8Silane30heat_PC-A	pH7-Silane-30-heat_254	A	pH7-Silane-30-heat_254-A	Intensity	30silaneheat	14094.0	1302	1302	1302
21	30_pH8Silane30heat_PC-B	pH7-Silane-30-heat_254	B	pH7-Silane-30-heat_254-B	Intensity	30silaneheat	13650.0	1258	1258	1258
22	31_pH8Silane30heat_PC-C	pH7-Silane-30-heat_254	C	pH7-Silane-30-heat_254-C	Intensity	30silaneheat	13611.0	1285	1285	1285
23	32_pH8Silane30heat_PC-D	pH7-Silane-30-heat_254	D	pH7-Silane-30-heat_254-D	Intensity	30silaneheat	13778.0	1532	1532	1532
24	41_pH8Silane30obd_NC-A	pH7-Silane-30-obd_nCL	A	pH7-Silane-30-obd_nCL-A	Intensity	30silaneobd	13320.0	833	833	833
25	42_pH8Silane30obd_NC-B	pH7-Silane-30-obd_nCL	B	pH7-Silane-30-obd_nCL-B	Intensity	30silaneobd	13634.0	857	857	857
26	43_pH8Silane30obd_NC-C	pH7-Silane-30-obd_nCL	C	pH7-Silane-30-obd_nCL-C	Intensity	30silaneobd	13214.0	846	846	846
27	44_pH8Silane30obd_NC-D	pH7-Silane-30-obd_nCL	D	pH7-Silane-30-obd_nCL-D	Intensity	30silaneobd	13933.0	1072	1072	1072
28	46_pH8Silane30obd_PC-B	pH7-Silane-30-obd_254	B	pH7-Silane-30-obd_254-B	Intensity	30silaneobd	11263.0	994	994	994
29	47_pH8Silane30obd_PC-C	pH7-Silane-30-obd_254	C	pH7-Silane-30-obd_254-C	Intensity	30silaneobd	16089.0	1219	1219	1219
30	48_pH8Silane30obd_PC-D	pH7-Silane-30-obd_254	D	pH7-Silane-30-obd_254-D	Intensity	30silaneobd	16276.0	1521	1521	1521
31	46_pH8Silane30obd_PC-F	pH7-Silane-30-obd_254	F	pH7-Silane-30-obd_254-F	Intensity	30silaneobd	16247.0	1219	1219	1219

200826 Expt.296: Sequence-Agnostic RNP Purification¶

Table of Contents

1. Import Modules and Files¶

2. Metadata Creation¶

3. Re-Annotate the MaxQuant pGroups with stable Gene IDs¶

4. Review Contaminants by Sample¶

Results: GOOD¶

5. Assess Digestion Efficiency¶

Results: Average¶

6. Remove Contaminants¶

7. Drop Gene Duplicates and Filter Intensities by LFQ¶

8. Review Sample Clustering by Group¶

Results: INTERESTING¶

9. Analyse Normalisation Effects by Sample¶

Result: GOOD¶

10. Compare Intensity Distribution and Sequence Coverage¶

Result: GOOD¶

11. Compare Sum Peptide Counts¶

Results: Interesting¶

12. Compare Unique Gene Counts¶

Results: GOOD¶

13. Assess Replicate Correlation¶

Results: GOOD¶

14. Classify RBP¶

Results: GOOD¶

15. Compare RBP Identity Between Ethanol Treatments¶

Results: GOOD¶

16. Review Basic Gene Ontology¶

Result: GOOD¶

17. Retrieve GO Records¶

Results: GOOD¶

Results: GOOD¶

Conclusion¶

	GO Term	relationship	depth	go ID	Description	ctrl list type	parent(s)
0	binding	Molecular Function	1	GO:0005488	NaN	NaN	GO:0003674
1	protein binding	Molecular Function	2	GO:0005515	NaN	NaN	GO:0005488
2	chromatin binding	Molecular Function	2	GO:0003682	NaN	NaN	GO:0005488
3	nucleic acid binding	Molecular Function	3	GO:0003676	NaN	NaN	GO:0003676
4	DNA binding	Molecular Function	4	GO:0003677	NaN	general	GO:0003676
5	RNA binding	Molecular Function	4	GO:0003723	NaN	general	GO:0003676
6	DNA/RNA hybrid binding	Molecular Function	4	GO:0071667	NaN	general	GO:0003676
7	translation regulator activity	Molecular Function	4	GO:0090079	NaN	NaN	GO:0003676
8	regulatory region nucleic acid binding	Molecular Function	4	GO:0001067	NaN	NaN	GO:0003676